At the end of the 2022-2023 Football Season Footballer Productivity 2023 dashboard on Tableau Public. Please feel free to use this as I believe it will allow you to explore the data and find interesting insights!
The data collection was a little bit different to the last post as I decided to only select players from the following leagues (still using FBREF):
- English Premier League
- Spanish La Liga
- Italian Serie A
- German Bundesliga
- French Ligue 1
- Dutch Eredivisie
- Portuguese Liga
I then decided to select players who had 5+ Goals and Assists (G&A) from these leagues. For the purpose of these charts, I’ve only included players with 10+ G&A and defaulted the dashboard to this view. You can go into the Info menu to update this # to whatever you want, whether it’s looking at everyone or looking closer at the elite and making this higher. I also added in those players performances in European Competitions too and you can now look separately at Champions League, Europa League and Europa Conference League performance.
To reiterate the last post, the following analysis looks at attacking player’s Goals, Assists, Expected Goals (xG) and Expected Assists (xAG) to create differences and ratios that can help us look at players to see if they are over-performed last season when it comes to scoring goals and how much luck they might have when it comes to assisting and their team mates finishing their chances.
Firstly, before we begin lets quickly recap xG and xAG:
What is xG?
Very simply, xG (or expected goals) is the probability that a shot will result in a goal based on the characteristics of that shot and the events leading up to it. Some of these characteristics/variables include:
- Location of shooter: How far was it from the goal and at what angle on the pitch?
- Body part: Was it a header or off the shooter’s foot?
- Type of pass: Was it from a through ball, cross, set piece, etc?
- Type of attack: Was it from an established possession? Was it off a rebound?
Did the defense have time to get in position? Did it follow a dribble?
Every shot is compared to thousands of shots with similar characteristics to determine the probability that this shot will result in a goal. That probability is the expected goal total. An xG of 0 is a certain miss, while an xG of 1 is a certain goal. An xG of .5 would indicate that if identical shots were attempted 10 times, 5 would be expected to result in a goal.
npxG is the expected goals not including penalties. Penalties have an xG of 0.79.
What is xA (expected assists) and xAG (expected assisted goals)? How do they differ?
xA, or expected assists, is the likelihood that a given completed pass will become a goal assist. This statistic developed by Opta assigns a likelihood to all passes based on the type of the pass, the location on the pitch, the phase of play, and the distance covered. Players receive xA for every completed pass regardless of whether a shot occurred or not.
In order to just isolate the xG on passes that assist a shot, there’s Expected Assisted Goals (xAG). This indicates a player’s ability to set up scoring chances without having to rely on the actual result of the shot or the shooter’s luck/ability. Players receive xAG only when a shot is taken after a completed pass.
We use xG + xAG for goal contributions since players’ goal contributions are typically Goals + Assists and this better matches that standard.
For my analysis I will be using xG/npxG and xAG to create visualizations that hopefully give insight into how the top forwards from the top European leagues are performing this season.
Goals Difference vs Assists Difference
The below charts looks at goals difference and assists difference where by:
- Goals Difference = Goals – xG
- Assists Difference = Assists – xAG
Goals Difference:
A value of 0 indicates that the player is performing as expected.
A positive value means that they are over-performing their expected
A negative value means that they are under-performing their expected
Assists Difference:
A value of 0 indicates that the players team mates are performing when it comes to finishing the chances created by that player
A positive value means that the players team mates are over-performing when it comes to finishing the chances created by that player
A negative value means that the players team mates are under-performing when it comes to finishing the chances created by that player
Goals Ratio vs Assists Ratio
The below charts looks at goals ratio and assists ratio where by:
- Goals Ratio = Goals / xG
- Assists Ratio = Assists / xAG
Goals Ratio
A ratio of 1 indicates that the player is performing as expected.
A ratio above 1 means that they are over-performing their expected
A ratio below 1 means that they are under-performing their expected
Assists Ratio
A ratio of 1 indicates that the player’s teammates are performing as expected.
A ratio above 1 means that the player’s teammates are over-performing when it comes to finishing the chances created by that player
A ratio below 1 means that the player’s teammates are under-performing when it comes to finishing the chances created by that player
Performance Goals vs Assists
Expected Goals vs Assists
Looking at the ratio and difference charts we can see four quadrants are created as we use the fact that a difference of 0 or ratio of 1 means the player is performing as expected:
For this post, I will look at a few notable players from the differential charts:
Goals < xG and Assists < xAG (yellow players)
Players are scoring less than expected and have also been unlucky to not have more assists.
The notable stand out player here is Bruno Fernandes. By god, all United fans this season (myself included) will be hoping that Rasmus Hojlund will be our goal scoring saviour. Bruno last season, wasn’t his goal scoring self last season (9G vs 11.9 xG), meaning he should have probably had 3 more goals. However, his assist differential is staggering at 11.60 (11A vs 21.6 xAG) indicating he should have close to 12 more assists last season. His combined xG + xAG per 90 is 0.72, which is pretty remarkable. Hopefully, United can become more clinical for the upcoming season and let’s be honest they have to if they want be able to compete at the top of the Premier League. The next closest player to Bruno is Havertz, who should have had close to five more assists last season (1A vs.5.6 xAG) – maybe his luck will change at Arsenal!
Goals < xG and Assists > xAG (red players)
Players are scoring less than expected and have also been lucky to have as many assists as they do.
The two notable players I think that are here Christian Eriksen and Ciro Immobile. Eriksen only scored 1 goal last season versus an expected xG of 5.3 and also luckily had 10 assists compared to his xAG of 5.7, meaning his teammates were more than clinical finishing the chances he created! We know he took a deeper role last season and I do remember some chances he missed he probably could have done better on. With United adding Mason Mount (7 G&A vs 6.6 xG+xAG), could this see more limited play time for the Danish playmaker? Immobile on the other hand scored 13 goals vs 16.6 xG and was lucky to get 5 more assists than expected (7A vs 2.3xAG). I didn’t watch much Serie A last season, but I would be interested to hear from the Lazio fans on what they made of Immobile’s performances last season and whether or not they think he over or under performed?
Goals > xG and Assists < xAG (blue players)
Players are scoring more than expected and have also been unlucky to not have more assists.
Two players that stand out here are Harry Kane and Alex Baena. Kane was overshadowed last year a little bit by that Erling Haaland fella who went mental and broke the English scoring record in his first season. Despite this, Kane had another great season scoring 31 goals (25.1xG), 6 more than expected showing how clinical he is, but also notching up 5 assists (8.1 xAG) and being unlucky to not have a few more. Wherever he ends up in the next few weeks, one thing is certain – Harry Kane will score goals. I don’t know much about Alex Baena, but the data suggests he was very clinical last season 10 goals (5.2xG) and was also unlucky not to have more than 3 assists (7.5 xAG). After watching a YouTube compilation (like any decent researcher), it turns out he scored a bunch of worldies and might be the best the player I have ever seen. Without getting carried away and jokes aside, he had a really decent year and has been linked to a few clubs this summer with Aston Villa leading the race for him a month ago.
Goals > xG and Assists > xAG (green players)
Players are scoring more than expected and have also been lucky to have as many assists as they do.
Again, two players stand out here in Cody Gakpo and of course Erling Haaland. Let’s start out with Gakpo, if we separate his season into PSV vs Liverpool we can see just how phenomenal he was prior to move his in January with 9 goals (6.3 xG) and 12 assists (6.8 xAG) in 21 games. A staggering 1.68 G&A per 90 minutes. Although his team mates might have helped him out with a few assists, Gakpo had an incredible first half of last season. He might have slowed down after joining Liverpool, but registering 7 goals (6.2 xG) and 2 assists (2.7 xAG) in his first season in the Premier League under a miss-firing Liverpool is extremely respectable. I’m excited to see how Klopp tries to incorporate Gakpo, Nunez, Salah and the returning Jota and Diaz who missed most of last season.
What was it Jamie Carragher said about Haaland half way through last season? Oh yeah, that he ‘picked the wrong club’. I know he’s gone on to correct what he has said, but man…the guy scored 48 goals (39.1xG) and got 9 assists (6.1 xAG) in the Premier League and Champions League, as helped Man City to achieve the treble. That came out to 1.42 G&A per 90. Truly remarkable. It seems like Haaland is not the type of player to slow down either, it will be interesting to see how he performs this season now he has one year under his belt with Pep Guardiola.
With a few players discussed already I thought it would be really cool to pass the analysis over to you guys. If you have a player, team or league you wish to discuss about you can use the dashboard to find the differentials, ratios, performance and expected statistics to start discussion.